Abstract: In data-driven prognostic methods, the prediction
accuracy of the estimation for remaining useful life of bearings
mainly depends on the performance of health indicators, which
are usually fused some statistical features extracted from vibrating
signals. However, the existing health indicators have the following
two drawbacks: (1) The differnet ranges of the statistical features
have the different contributions to construct the health indicators,
the expert knowledge is required to extract the features. (2) When
convolutional neural networks are utilized to tackle time-frequency
features of signals, the time-series of signals are not considered.
To overcome these drawbacks, in this study, the method combining
convolutional neural network with gated recurrent unit is proposed to
extract the time-frequency image features. The extracted features are
utilized to construct health indicator and predict remaining useful life
of bearings. First, original signals are converted into time-frequency
images by using continuous wavelet transform so as to form the
original feature sets. Second, with convolutional and pooling layers
of convolutional neural networks, the most sensitive features of
time-frequency images are selected from the original feature sets.
Finally, these selected features are fed into the gated recurrent unit
to construct the health indicator. The results state that the proposed
method shows the enhance performance than the related studies which
have used the same bearing dataset provided by PRONOSTIA.
Abstract: In this paper, a model is proposed to determine the life
distribution parameters of the useful life region for the PV system
utilizing a combination of non-parametric and linear regression
analysis for the failure data of these systems. Results showed that this
method is dependable for analyzing failure time data for such reliable
systems when the data is scarce.
Abstract: Sedimentation in reservoirs lowers the quality of
consumed water, reduce the volume of reservoir, lowers the
controllable amount of flood, increases the risk of water overflow
during possible floods and the risk of reversal and reduction of dam's
useful life. So in all stages of dam establishment such as cognitive
studies, phase-1 studies of design, control, construction and
maintenance, the problem of sedimentation in reservoir should be
considered. What engineers need to do is examine and develop the
methods to keep effective capacity of a reservoir, however engineers
should also consider the influences of the methods on the flood
disaster, functions of water use facilities and environmental
issues.This article first examines the sedimentation in reservoirs and
shows how to control it and then discusses the studies about the
sedimens in Siazakh Dam.